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检索条件"任意字段=2012 IEEE Visual Communications and Image Processing, VCIP 2012"

共 124 条记录，以下是41-50 订阅

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LIGHTWEIGHT SEARCHABLE SCREEN VIDEO RECORDING

LIGHTWEIGHT SEARCHABLE SCREEN VIDEO RECORDING

引用

ieee visual communications and image processing (vcip)

作者： Marder, Mattias Geva, Amir Ruan, Yaoping IBM Res Haifa Haifa Israel IBM Res Thomas J Watson Res Ctr Haifa Israel

ISBN: (纸本)9781467344050;9781467344067

Command logging of maintenance and operation activities of modern computer systems has become an integral component of customer and audit requirements. In recent years, this logging has usually been achieved via desktop video recording. However, the conventional approach of video recording requires high computation overhead, high network bandwidth, and a large storage size. Searching through video files is also a challenge. In this paper, we present a lossy, but text text-preserving, compression scheme that meets these challenges by creating a sparse bitonal image suitable for optical character recognition (OCR). Using our system for auditing, the bitonal image gets stored on a server. Due to the mechanism's text-preserving compression, we can apply OCR off-line to create annotations of each video frame, making the output searchable. Compared to state-of-the-art compression of raw video, our approach can reduce file size by 50-80%, while using CPU and memory resources similar to other methods.

关键词： Text segmentation screen image binarization audit screen recording monitoring

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SEQM: EDGE QUALITY ASSESSMENT BASED ON STRUCTURAL PIXEL MATCHING

SEQM: EDGE QUALITY ASSESSMENT BASED ON STRUCTURAL PIXEL MATC...

引用

ieee visual communications and image processing (vcip)

作者： Jang, Won-Dong Kim, Chang-Su Korea Univ Sch Elect Engn Seoul South Korea

ISBN: (纸本)9781467344050;9781467344067

A novel quality metric for binary edge maps, called the structural edge quality metric (SEQM), is proposed in this work. First, we define the matching cost between an edge pixel in a detected edge map and its candidate matching pixel in the ground-truth edge map. The matching cost includes a structural term, as well as a positional term, to measure the discrepancy between the local structures around the two pixels. Then, we determine the optimal matching pairs of pixels using the graph-cut optimization, in which a smoothness term is employed to take into account global edge structures in the matching. Finally, we sum up the matching costs of all edge pixels to determine the quality index of the detected edge map. Simulation results demonstrate that the proposed SEQM provides more faithful and reliable quality indices than conventional metrics.

关键词： image quality assessment edge quality assessment binary edge map structural similarity pixel matching

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A NOVEL PARTICLE FILTERING FRAMEWORK FOR 2D-TO-3D CONVERSION FROM A MONOSCOPIC 2D image SEQUENCE

A NOVEL PARTICLE FILTERING FRAMEWORK FOR 2D-TO-3D CONVERSION...

引用

ieee visual communications and image processing (vcip)

作者： Huang, Jing Schonfeld, Dan Univ Illinois Dept Elect & Comp Engn Chicago IL 60680 USA

ISBN: (纸本)9781467344050;9781467344067

This paper presents a novel 2D-TO-3D conversion approach from a monoscopic 2D image sequence. We propose a particle filter framework for recursive recovery of point-wise depth from feature correspondences matched through image sequences. We formulate a novel 2D dynamics model for recursive depth estimation with the combination of camera model, structure model and translation model. The proposed method utilizes edge-detection-assisted scale-invariant features to avoid lack of edge features in scale-invariant features (SIFT). Furthermore, the depths in the depth map are computed and interpolated using 2D Delaunay triangulation. Finally, a stereo-view generation algorithm is presented for multiple users that uses proposed dynamics model and particle filter framework. Experimental results show that our proposed framework yields superior results.

关键词： Particle filtering depth estimation image motion analysis feature matching interpolation

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OPTIMIZING JPEG QUANTIZATION TABLE FOR LOW BIT RATE MOBILE visual SEARCH

OPTIMIZING JPEG QUANTIZATION TABLE FOR LOW BIT RATE MOBILE V...

引用

ieee visual communications and image processing (vcip)

作者： Duan, Ling-Yu Liu, Xiangkai Chen, Jie Huang, Tiejun Gao, Wen Peking Univ Inst Digital Media Sch EE&CS Beijing 100871 Peoples R China

ISBN: (纸本)9781467344050

Smart phones is bringing about emerging potentials in mobile visual search. Extensive research efforts have been made in compact visual descriptors. However, directly extracting visual descriptors on a mobile device is computationally intensive and time consuming. Towards low bit rate visual search, we propose to deeply compress query images by learning a customized JPEG quantization table in the context of visual search. Distinct from traditional image compression, by incorporating pair-wise image matching precision into distortion measure, we optimize quantization table to seek a better trade-off between image compression rate and visual search performance. An evolutionary algorithm is employed to learn an optimal quantization table. Under MPEG CDVS evaluation framework, extensive evaluation has been done including image retrieval and pair-wise matching over 1 million database images. Experimental results have demonstrated that our optimized quantization table works much better than JPEG default one in terms of retrieval/matching performance vs. a set of different operating points. The proposed low bit rate solution may be easily deployed to smart phones without hardware support, as a useful complement to the ongoing MPEG CDVS standardization efforts.

关键词： Mobile visual search image compression Quantization table image matching image retrieval

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A LOW-COMPLEXITY SCREEN COMPRESSION SCHEME

A LOW-COMPLEXITY SCREEN COMPRESSION SCHEME

引用

ieee visual communications and image processing (vcip)

作者： Pan, Zhaotai Shen, Huifeng Lu, Yan Yu, Nenghai Li, Shipeng Univ Sci & Technol China Hefei 230026 Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781467344050;9781467344067

This paper presents a block-based low-complexity screen compression scheme. In this scheme, the input screen is split into non-overlapping blocks which are classified as pictorial blocks and textual blocks. We design a low-complexity yet efficient algorithm to compress the textual blocks. We use base colors plus escape pixels to represent and quantize the text pixels, and such quantization mechanism not only achieves high compression efficiency but also keeps low encoding/decoding complexity. We also propose the two-direction predictive index coding and hierarchical pattern coding technologies to utilize the local spatial correlation and the global pattern correlation for text pixels. In addition, to utilize the correlation between the luminance and chrominance channels, we propose a joint-channel index coding method to further improve the compression efficiency. The compression efficiency and complexity of the proposed method is evaluated against the popular image codecs JPEG/JPEG200 and PNG, a recent published screen compression scheme HJPC as well as the popular video codec H.264.

关键词： Screen compression low-complexity compression compound image compression

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CONTEXT-AWARE SINGLE image SUPER-RESOLUTION USING LOCALITY-CONSTRAINED GROUP SPARSE REPRESENTATION

CONTEXT-AWARE SINGLE IMAGE SUPER-RESOLUTION USING LOCALITY-C...

引用

ieee visual communications and image processing (vcip)

作者： Tsai, Chih-Yun Huang, De-An Yang, Min-Chun Kang, Li-Wei Wang, Yu-Chiang Frank Acad Sinica Res Ctr Informat Technol Innovat Taipei 115 Taiwan Natl Taiwan Univ Dept Elect Engn Taipei Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Math Taipei Taiwan Acad Sinica Inst Sci Informat Taipei Taiwan

ISBN: (纸本)9781467344050;9781467344067

We present a novel learning-based method for single image super-resolution (SR). Given a single input low-resolution (LR) image (and its image pyramid), we propose to learn context-specific image sparse representation, which aims at modeling the relationship between low and high-resolution image patch pairs of different context categories in terms of the learned dictionaries. To predict the SR image, we derive the context-specific sparse representation of each image patch in the LR input with additional locality and group sparsity constraints. While the locality constraint searches for the most similar image patches and uses the corresponding high-resolution outputs for SR, the group sparsity constraint allows us to utilize the information from most relevant context categories for predicting the final SR output. Experimental results show the proposed method is able to quantitatively and qualitatively achieve state-of-the-art performance.

关键词： Super-resolution sparse representation self-learning group Lasso data locality

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EDGE MODELING PREDICTION FOR COMPUTED TOMOGRAPHY imageS

EDGE MODELING PREDICTION FOR COMPUTED TOMOGRAPHY IMAGES

引用

ieee visual communications and image processing (vcip)

作者： Weinlich, Andreas Amon, Peter Hutter, Andreas Kaup, Andre Univ Erlangen Nurnberg D-91054 Erlangen Germany Siemens Corp Technol Imaging & Comp Vis Munich Germany

ISBN: (纸本)9781467344050;9781467344067

Predictive coding is applied in many state-of-the-art loss-less image compression algorithms like JPEG-LS, CALIC, or least-squares-based methods. We propose a new approach for accurate intensity prediction in pixel-predictive coding of computed tomography (CT) images. Exploiting their particular edge characteristic, the method only relies on a small twelve-pixel context. It does neither require adaptation to larger-region image characteristics nor the transmission of side-information and therefore may be particularly suitable for compression of small images like in region-of-interest coding. While applying simple linear prediction with fixed weights in homogeneous regions, a Gauss error model-function is fit to given contexts in edge regions and then sampled at the position corresponding to the pixel to be predicted in order to obtain prediction values. By the example of CALIC, it is shown that for CT data the edge modeling prediction (EMP) approach can yield an even smaller prediction error than other methods relying on context modeling.

关键词： Edge modeling prediction predictive coding lossless medical image compression X-ray computed tomography Gauss error function fitting

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OPTIMIZATION OF GENERALIZED LT CODES FOR PROGRESSIVE image TRANSFER

OPTIMIZATION OF GENERALIZED LT CODES FOR PROGRESSIVE IMAGE T...

引用

ieee visual communications and image processing (vcip)

作者： Arslan, Suayb S. Cosman, Pamela C. Milstein, Laurence B. Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92103 USA

ISBN: (纸本)9781467344050;9781467344067

Rateless codes allow a user to incrementally send additional redundancy, so they can be useful for heterogeneous and time-varying networks for which the choice of redundancy level in advance is difficult. Rateless codes are an attractive application layer forward error correction solution due to their flexibility and capacity-approaching performance. The original rateless codes were developed for the delivery of equally important information. In many multimedia applications, some data symbols are more important than others. Unequal error protection (UEP) designs are attractive solutions for such transmissions. However previous UEP rateless code designs were aimed for coarsely layered sources and might exhibit poor performance for fine-grained progressive coding. The main objective of this paper is to introduce a more generalized coding scheme, parameters of which can be tailored for progressive multimedia transmission. We present the optimization of a generalized rateless code using two different progressive source transmission protocols. Proposed coding scheme is shown to exhibit better unequal protection and recovery time properties than other published results.

关键词： Progressive sources iterative decoding rateless codes unequal error protection unequal recovery and iteration time

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INTEGRATING STEREOSCOPIC image TRANSCODING WITH RETARGETING FOR MOBILE STREAMING

INTEGRATING STEREOSCOPIC IMAGE TRANSCODING WITH RETARGETING ...

引用

ieee visual communications and image processing (vcip)

作者： Liu, Yanwei Ci, Song Liu, Jinxia Ye, Yun Qi, Ying Chinese Acad Sci Inst Acoust Beijing Peoples R China Univ Nebraska Lincoln Omaha NE 68182 USA Zhejiang Wanli Univ Ningbo Peoples R China

ISBN: (纸本)9781467344050;9781467344067

With the progresses in 3D imaging technologies, mobile stereoscopic images are gradually popular since it can provide the anytime and anywhere 3D viewing effects. Due to the heterogeneous transmission environments and various screen sizes of mobile devices, the stereoscopic image transcoding and retargeting are two completely independent adaptation techniques for mobile 3D image streaming. These two processing operations both deal with the disparity estimation. In this work, we propose to integrate stereoscopic 3D image transcoding with retargeting for mobile streaming. The stereoscopic image transcoding and retargeting are coupled through a straightforward link that provides the pixel-based disparity map from the retargeting stage to guide the transcoding for saving some computations while keeping efficient bit allocation. Our experimental results clearly demonstrate the advantage of coupling the transcoding with retargeting in terms of faster transcoding and saliency-based bit allocation for low bit-rate mobile streaming.

关键词： stereoscopic retargeting stereoscopic 3D image transcoding mobile 3D image streaming

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A LOSSLESS COLOR image COMPRESSION METHOD BASED ON A NEW REVERSIBLE COLOR TRANSFORM

A LOSSLESS COLOR IMAGE COMPRESSION METHOD BASED ON A NEW REV...

引用

ieee visual communications and image processing (vcip)

作者： Kim, Seyun Cho, Nam Ik Seoul Natl Univ Dept Elect Engn & Comp Sci INMC Seoul 151744 South Korea

ISBN: (纸本)9781467344050

In many conventional lossless color image compression methods, the pixels or lines from each color component are interleaved, and then they are predicted and coded. Also, it has been reported that the reversible color transform (RCT) followed by a grayscale encoder gives higher coding gain than the independent compression of each channel does. In this paper, we propose a lossless color image compression method that concentrates on the efficient coding of chrominance channels with a new color transform and hierarchical coding of chrominance channel pixels. Specifically, we first transform an input image with R, G, and B color space into YCu C-v color space using the proposed RCT, which shows better decorrelation performance than the existing RCT. After the color transformation, the luminance channel Y is compressed by a conventional lossless image coder, such as JPEG-LS, CALIC, or JPEG2000 lossless. Unlike the luminance channel, the chrominance channels C-u and C-v are relatively smooth and have different statistical characteristic. Therefore, the chrominance channels are differently encoded based on a hierarchical decomposition and directional prediction. Finally, effective context modeling for prediction residuals is adopted. Experimental results show that the proposed method improves the compression performance by 40% over the conventional channel independent compression methods and 5% over the existing methods that exploit the channel correlation.

关键词： lossless color image compression reversible color transform lifting hierarchical coding

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